Abstract
This research is aimed to detect defects on the surface of the fabric and deep learning model optimization. Since defect detection cannot effectively solve the fabric with complex background by image processing, this research uses deep learning to identify defects. However, the current network architecture mainly focuses on natural images rather than the defect detection. As a result, the network architecture used for defect detection has more redundant neurons, which reduces the inference speed. In order to solve the above problems, we propose network pruning with the Bayesian optimization algorithm to automatically tune the network pruning parameters, and then retrain the network after pruning. The training and detection process uses the above-mentioned pruning network to predict the defect feature map, and then uses the image processing flow proposed in this research for the final judgment during fabric defect detection. The proposed method is verified in the two self-made datasets and the two public datasets. In the part of the proposed network optimization results, the Intersection over Union (IoU) of four datasets are dropped by 1.26%, 1.13%, 1.21%, and 2.15% compared to the original network model, but the inference time is reduced to 20.84%, 40.52%, 23.02%, and 23.33% of the original network model using Geforce 2080 Ti. Furthermore, the inference time is also reduced to 17.56%, 37.03%, 19.67%, and 22.26% using the embedded system AGX Xavier. After the image processing part, the accuracy of the four datasets can reach 92.75%, 94.87%, 95.6%, and 81.82%, respectively. In this research, Yolov4 is also trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects.
Highlights
Published: 25 October 2021Fabric is a daily necessity for people
Yolov4 is trained with fabric defects, and the results showed this model are not conducive to detecting long and narrow fabric defects
The automated optical inspection in fabric manufacturing was developed to achieve the accuracy in defect detection, and reduce the cost of inspection and improve the product quality
Summary
Based on the research proposed by Selvi et al [1], fabric with signs of defects tends to bring its selling price down by 45% to 65%, seriously affecting the selling price. By implementing defect detection methods, defective items can be eliminated beforehand in order to reduce the amount of defective fabric so as to elevate the product quality and shape a stronger brand value. For this reason, fabric defect detection plays an essential role in the modern textile manufacturing process. Fabric defect detection is mainly executed manually. According to the [1], the inspecting accuracy of manual inspection is around 70% and below the acceptable rate of 80%
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